Advancing Managed Bio-Inspired Collectives: Using an Analytical Model to Design and Evaluate Interfaces

Abstract

This proposed one-year project is intended to bridge the two most promising results from ONR Project N0001418-1-2503 and a future proposal that will explicitly design and analyze human interaction with a Dynamic Robot Aggregates (DRAgs). A DRAg is a blend of (a) distributed multi-robot systems modeled using multi-agent autonomy with (b) bio-inspired robot collectives, including spatial swarms and hub-based robot colonies. The prior project yielded two very promising results, a new graph-based formalism for modeling agent-based solutions to the best-of-N problem, and a new operationalization of the notion of transparency applied to human-DRAg interaction. The proposed research makes two claims for a specic type of DRAg, one with four independent hub-based colonies: (1) Convergence rates, success probabilities, and condence bounds can be derived for human-DRAg interaction from the discrete-time Markov chains produced by the graph-based formalism. (2) The graph-based formalism facilitates a new prediction method that will enable new transparent interface designs for human interaction with hub-based colonies. These claims will be validated through a human factors study that evaluates the design of transparent interfaces supported by the graph-based formalism.

Document Details

Document Type
DoD Grant Award
Publication Date
Mar 15, 2021
Source ID
N000142112190

Entities

People

  • Michael A Goodrich

Organizations

  • Brigham Young University
  • Office of Naval Research
  • United States Navy

Tags

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Distributed Systems and Data Platform Development
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control
  • Autonomy - Human-Robot Interaction